75 citations · 820 across the 81 of their papers we have counts for
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Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes
Jian Qian, Ronan Fruit, Matteo Pirotta +1
We introduce and analyse two algorithms for exploration-exploitation in discrete and continuous Markov Decision Processes (MDPs) based on exploration bonuses. SCAL is a variant…
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes
Ronan Fruit, Matteo Pirotta, Alessandro Lazaric
While designing the state space of an MDP, it is common to include states that are transient or not reachable by any policy (e.g., in mountain car, the product space of speed and p…
Distributed Adaptive Sampling for Kernel Matrix Approximation
Daniele Calandriello, Alessandro Lazaric, Michal Valko
Most kernel-based methods, such as kernel or Gaussian process regression, kernel PCA, ICA, or -means clustering, do not scale to large datasets, because constructing and storing…
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
Ronan Fruit, Matteo Pirotta, Alessandro Lazaric +1
We introduce SCAL, an algorithm designed to perform efficient exploration-exploitation in any unknown weakly-communicating Markov decision process (MDP) for which an upper bound $c…